Interpretation#

Load Data#

import pandas as pd

df = pd.read_csv('data/tag_df_final.csv')

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Input In [1], in <cell line: 3>()
      1 import pandas as pd
----> 3 df = pd.read_csv('data/tag_df_final.csv')

File ~\anaconda3\envs\steam_nlp\lib\site-packages\pandas\util\_decorators.py:211, in deprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper(*args, **kwargs)
    209     else:
    210         kwargs[new_arg_name] = new_arg_value
--> 211 return func(*args, **kwargs)

File ~\anaconda3\envs\steam_nlp\lib\site-packages\pandas\util\_decorators.py:331, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    325 if len(args) > num_allow_args:
    326     warnings.warn(
    327         msg.format(arguments=_format_argument_list(allow_args)),
    328         FutureWarning,
    329         stacklevel=find_stack_level(),
    330     )
--> 331 return func(*args, **kwargs)

File ~\anaconda3\envs\steam_nlp\lib\site-packages\pandas\io\parsers\readers.py:950, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)
    935 kwds_defaults = _refine_defaults_read(
    936     dialect,
    937     delimiter,
   (...)
    946     defaults={"delimiter": ","},
    947 )
    948 kwds.update(kwds_defaults)
--> 950 return _read(filepath_or_buffer, kwds)

File ~\anaconda3\envs\steam_nlp\lib\site-packages\pandas\io\parsers\readers.py:605, in _read(filepath_or_buffer, kwds)
    602 _validate_names(kwds.get("names", None))
    604 # Create the parser.
--> 605 parser = TextFileReader(filepath_or_buffer, **kwds)
    607 if chunksize or iterator:
    608     return parser

File ~\anaconda3\envs\steam_nlp\lib\site-packages\pandas\io\parsers\readers.py:1442, in TextFileReader.__init__(self, f, engine, **kwds)
   1439     self.options["has_index_names"] = kwds["has_index_names"]
   1441 self.handles: IOHandles | None = None
-> 1442 self._engine = self._make_engine(f, self.engine)

File ~\anaconda3\envs\steam_nlp\lib\site-packages\pandas\io\parsers\readers.py:1735, in TextFileReader._make_engine(self, f, engine)
   1733     if "b" not in mode:
   1734         mode += "b"
-> 1735 self.handles = get_handle(
   1736     f,
   1737     mode,
   1738     encoding=self.options.get("encoding", None),
   1739     compression=self.options.get("compression", None),
   1740     memory_map=self.options.get("memory_map", False),
   1741     is_text=is_text,
   1742     errors=self.options.get("encoding_errors", "strict"),
   1743     storage_options=self.options.get("storage_options", None),
   1744 )
   1745 assert self.handles is not None
   1746 f = self.handles.handle

File ~\anaconda3\envs\steam_nlp\lib\site-packages\pandas\io\common.py:856, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
    851 elif isinstance(handle, str):
    852     # Check whether the filename is to be opened in binary mode.
    853     # Binary mode does not support 'encoding' and 'newline'.
    854     if ioargs.encoding and "b" not in ioargs.mode:
    855         # Encoding
--> 856         handle = open(
    857             handle,
    858             ioargs.mode,
    859             encoding=ioargs.encoding,
    860             errors=errors,
    861             newline="",
    862         )
    863     else:
    864         # Binary mode
    865         handle = open(handle, ioargs.mode)

FileNotFoundError: [Errno 2] No such file or directory: 'data/tag_df_final.csv'

Viewing the top 10 game aspects tagged with a non-neutral sentiment polarity reveals that ‘game’ has by far the highest frequency.

Given the lack of specific information this provides, it will be useful to preclude this token from subsequent charts.

(df[df['sentiment']!=0]
     .groupby('aspect',as_index=False)['description']
     .count().rename(columns={'description':'Count'})
     .sort_values(by='Count',ascending=False)
     .head(10)).reset_index(drop=True)
aspect Count
0 game 11527
1 campaign 2120
2 people 1172
3 multiplayer 1125
4 map 1065
5 play 875
6 crash 809
7 player 707
8 war 681
9 system 678

Aspect Frequency#

Charting the most frequent aspects tagged with a non-neutral score, it is clear how divisive many aspects of the game design are for the player base.

Several aspects feature in the top 20 of both sentiment lists which is indicative of the ‘mixed’ review score that game currently has on the Steam store.

While the campaign features promintently in both lists, the more than 1600 positive references far outstrip the number of negative references made in user reviews.

Similarly, the over 350 negative references to the game’s multiplayer aspect, is outweighed by almost 800 positive references.

The remainder of the overlapping aspects of design seem far more divisive and demonstrate more even splits in player opinion.

#%matplotlib inline
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
pos = (df[(df['sentiment']>0) & (df['aspect']!='game')]
                    .groupby('aspect')[['sentiment']]
                    .count()
                    .sort_values(by='sentiment',ascending=False)
                    .reset_index()
                    .head(20))
                    
sns.catplot(data = pos,
            y='aspect',
            x='sentiment',
            kind='bar',
            palette = ['#88D8B0'],
            height = 6,
           aspect = 1.5)

plt.title('Positive Game Aspects',fontsize=14)
plt.tick_params(labelsize=12)
plt.ylabel('Descriptor',fontsize=12)
plt.xlabel('Count',fontsize=12)
plt.xticks(list(range(0,1701,100)))
plt.tight_layout()
plt.show();

neg = (df[(df['sentiment']<0) & (df['aspect']!='game')]
                    .groupby('aspect')[['sentiment']]
                    .count()
                    .sort_values(by='sentiment',ascending=False)
                    .reset_index()
                    .head(20))

sns.catplot(data = neg,
            y='aspect',
            x='sentiment',
            kind='bar',
            palette = ['#FF6F69'],
            height = 6,
           aspect = 1.5)
plt.title('Negative Game Aspects',fontsize=14)
plt.tick_params(labelsize=12)
plt.ylabel('Descriptor',fontsize=12)
plt.xlabel('Count',fontsize=12)
plt.xticks(list(range(0,1701,100)))
plt.tight_layout()
plt.show();
../_images/7_Interpretation-Copy1_5_0.png ../_images/7_Interpretation-Copy1_5_1.png 
import numpy as np
import holoviews as hv
from holoviews import opts, dim

hv.extension('bokeh')
hv.output(size=300)

# create list of aspects from top pos and neg lists
aspect_list = set(pos['aspect'].to_list() + neg['aspect'].to_list())

# Create df of top aspects across pos and neg sentiments
top_df = df[(df['aspect'].isin(aspect_list))]

desc_counts = (top_df
     .groupby(by=['description'])[['sentiment']]
     .count()
     .rename(columns={'sentiment':'Count'})
     .sort_values(by ='Count', ascending =False)
     .reset_index())

top_df = top_df = df[(df['aspect'].isin(aspect_list)) & (df['description'].isin(desc_counts[desc_counts['Count']>149]['description']))]

links = (top_df
     .groupby(by=['aspect','description'],as_index=False)[['sentiment']]
     .count()
     .rename(columns={'sentiment':'Count'}))
         
         

# specify node names
#nodes = list(set(links['aspect'].tolist() + links['description'].tolist()))
nodes = list(set(links['aspect'].tolist()))
nodes.extend(set(links['description'].tolist()))
nodes = hv.Dataset(pd.DataFrame(nodes, columns = ['Token']))

def rotate_label(plot, element):
    white_space = "              "
    angles = plot.handles['text_1_source'].data['angle']
    characters = np.array(plot.handles['text_1_source'].data['text'])
    plot.handles['text_1_source'].data['text'] = np.array([x + white_space if x in characters[np.where((angles < -1.5707963267949) | (angles > 1.5707963267949))] else x for x in plot.handles['text_1_source'].data['text']])
    plot.handles['text_1_source'].data['text'] = np.array([white_space + x if x in characters[np.where((angles > -1.5707963267949) | (angles < 1.5707963267949))] else x for x in plot.handles['text_1_source'].data['text']])
    angles[np.where((angles < -1.5707963267949) | (angles > 1.5707963267949))] += 3.1415926535898
    plot.handles['text_1_glyph'].text_align = "center"

# create chord diagram
chord = hv.Chord((links, nodes)).select(Count=(25, None))
chord.opts(
    opts.Chord(title='Reltionships Between Aspects and Tokens', labels = 'Token', label_text_font_size='12pt', 
               node_color='Token', node_cmap=['#c1c1c1','#adadad'],node_size=10,
               edge_color='aspect', edge_cmap=['#FF6F69','#88D8B0','#ffcc5c'],
               hooks=[rotate_label], edge_alpha=0.8, edge_line_width=1)
)

label_data = chord.nodes.data label_data[‘rotation’] = np.arctan((label_data.y / label_data.x))

label_data[‘y’] = label_data[‘y’].apply(lambda x: x * 1.1) label_data[‘x’] = label_data[‘x’].apply(lambda x: x * 1.1)

labels = hv.Labels(label_data) labels.opts( opts.Labels(cmap=’magma’, text_font_size=’10pt’,padding=0.08, angle= dim(‘rotation’) * 1260/22 )) chord * labels

# create df counting all links between aspect and sentiment
counts_df = (df[(df['sentiment']!=0) & (df['aspect']!='game') & (df['aspect'].isin(aspect_list))]
    .groupby(by=['aspect','description'])[['sentiment']]
    .count()
    .rename(columns={'sentiment':'Count'})
    .sort_values(by ='Count', ascending =False)
    .reset_index()
)

# restrict to counts >20
counts_df = counts_df[counts_df['Count']>=20]

# create adjacency matrices for only tokens in counts_df
df_adj = df[df['aspect'].isin(set(df.aspect).intersection(set(counts_df.aspect))) & df['description'].isin(set(df.description).intersection(set(counts_df.description)))]
# adj1 (x,y)
adj1 = pd.crosstab(df_adj.description,(df_adj.aspect))
idx = adj1.columns.union(adj1.index)
adj1 = adj1.reindex(index=idx,columns=idx)
# adj2 (y,x)
adj2 = pd.crosstab(df_adj.aspect,df_adj.description)
idx = adj2.columns.union(adj2.index)
adj2 = adj2.reindex(index=idx,columns=idx)
# merge to replace & make symetrical
adj = adj1.fillna(adj2)

links = adj.to_numpy()
nodes = list(adj.columns)

import mne
from mne.viz import circular_layout
from mne_connectivity.viz import plot_connectivity_circle

start, end = 45, 135
first_half = (np.linspace(start, end, len(nodes)//2) +90).astype(int)[::+1] %360
second_half = (np.linspace(start, end, len(nodes)//2) -90).astype(int)[::-1] %360
node_angles = np.array(list(first_half) + list(second_half))

fig, ax =  plt.subplots(figsize=(20, 20), facecolor='black',subplot_kw=dict(polar=True))
plot_connectivity_circle(links, nodes, interactive= True, ax=ax)
fig.tight_layout()

# Create df of top aspects across pos and neg sentiments
top_df = df[(df['aspect'].isin(pos['aspect'])) | (df['aspect'].isin(neg['aspect']))]
# groupby to create count of descripto/sentiment links
top_df =(top_df
     .groupby(by=['aspect','description'])[['sentiment']]
     .count()
     .rename(columns={'sentiment':'Count'})
     .sort_values(by ='Count', ascending =False)
     .reset_index())
# restrict to links greater than 20 occurrences 
top_df = top_df[top_df['Count']>=20]

# create adjacency matrices for only tokens in top_df
df_adj = df[df['aspect'].isin(set(df.aspect).intersection(set(top_df.aspect))) & df['description'].isin(set(df.description).intersection(set(top_df.description)))]
# adj1 (x,y)
adj1 = pd.crosstab(df_adj.description,(df_adj.aspect))
idx = adj1.columns.union(adj1.index)
adj1 = adj1.reindex(index=idx,columns=idx)
# adj2 (y,x)
adj2 = pd.crosstab(df_adj.aspect,df_adj.description)
idx = adj2.columns.union(adj2.index)
adj2 = adj2.reindex(index=idx,columns=idx)
# merge to replace & make symetrical
adj = adj1.fillna(adj2)

# define array of links
links = adj.to_numpy()

#define names of nodes
nodes = list(adj.columns)

label_names = set(top_df['aspect'].to_list() + top_df['description'].to_list())
lh_labels = list(set(top_df['aspect']))
rh_labels = list(set(top_df['description']))

node_order = list()
node_order.extend(lh_labels[::-1])  # reverse the order
node_order.extend(rh_labels)

node_angles = circular_layout(label_names, node_order, start_pos=90,
                              group_boundaries=[0, len(label_names) / 2])

node_angles = circular_layout(label_names, node_order, start_pos=270,
                              group_boundaries=[0, len(lh_labels)])

fig, axes = plot_connectivity_circle(links, label_names, 
    node_angles=node_angles)

node_angles = circular_layout(label_names, node_order, start_pos=0,
                              group_boundaries=[0, len(lh_labels)])

fig, axes = plot_connectivity_circle(links, label_names, 
    node_angles=node_angles)

# Create df of top aspects across pos and neg sentiments
top_df = df[(df['aspect'].isin(pos['aspect'])) | (df['aspect'].isin(neg['aspect']))]

top_df = (top_df
     .groupby(by=['description'])[['sentiment']]
     .count()
     .rename(columns={'sentiment':'Count'})
     .sort_values(by ='Count', ascending =False)
     .reset_index())

top_df = df[(df['aspect'].isin(aspect_list)) & (df['description'].isin(top_df[top_df['Count']>99]['description']))]

# create adjacency matrices for only tokens in top_df
df_adj = df[df['aspect'].isin(set(df.aspect).intersection(set(top_df.aspect))) & df['description'].isin(set(df.description).intersection(set(top_df.description)))]
# adj1 (x,y)
adj1 = pd.crosstab(df_adj.description,(df_adj.aspect))
idx = adj1.columns.union(adj1.index)
adj1 = adj1.reindex(index=idx,columns=idx)
# adj2 (y,x)
adj2 = pd.crosstab(df_adj.aspect,df_adj.description)
idx = adj2.columns.union(adj2.index)
adj2 = adj2.reindex(index=idx,columns=idx)
# merge to replace & make symetrical
adj = adj1.fillna(adj2)

# define array of links
links = adj.to_numpy()

#define names of nodes
nodes = list(adj.columns)

label_names = set(top_df['aspect'].to_list() + top_df['description'].to_list())
lh_labels = list(set(top_df['aspect']))
rh_labels = list(set(top_df['description']))

node_order = list()
node_order.extend(lh_labels[::-1])  # reverse the order
node_order.extend(rh_labels)

node_angles = circular_layout(label_names, node_order, start_pos=90,
                              group_boundaries=[0, len(label_names) / 2])

node_angles = circular_layout(label_names, node_order, start_pos=270,
                              group_boundaries=[0, len(lh_labels)])

fig, axes = plot_connectivity_circle(links, label_names, 
    node_angles=node_angles)